Eigenvalue-based Cyclostationary Spectrum Sensing Using Multiple Antennas

In this paper, we propose a signal-selective spectrum sensing method for cognitive radio networks and specifically targeted for receivers with multiple-antenna capability. This method is used for detecting the presence or absence of primary users based on the eigenvalues of the cyclic covariance matrix of received signals. In particular, the cyclic correlation significance test is used to detect a specific signal-of-interest by exploiting knowledge of its cyclic frequencies. The analytical threshold for achieving constant false alarm rate using this detection method is presented, verified through simulations, and shown to be independent of both the number of samples used and the noise variance, effectively eliminating the dependence on accurate noise estimation. The proposed method is also shown, through numerical simulations, to outperform existing multiple-antenna cyclostationary-based spectrum sensing algorithms under a quasi-static Rayleigh fading channel, in both spatially correlated and uncorrelated noise environments. The algorithm also has significantly lower computational complexity than these other approaches.Comment: 6 pages, 6 figures, accepted to IEEE GLOBECOM 201

Spectrum measurement, sensing, analysis and simulation in the context of cognitive radio

The radio frequency (RF) spectrum is a scarce natural resource, currently regulated locally by national agencies. Spectrum has been assigned to different services and it is very difficult for emerging wireless technologies to gain access due to rigid spectmm policy and heavy opportunity cost. Current spectrum management by licensing causes artificial spectrum scarcity. Spectrum monitoring shows that many frequencies and times are unused. Dynamic spectrum access (DSA) is a potential solution to low spectrum efficiency. In DSA, an unlicensed user opportunistically uses vacant licensed spectrum with the help of cognitive radio. Cognitive radio is a key enabling technology for DSA. In a cognitive radio system, an unlicensed Secondary User (SU) identifies vacant licensed spectrum allocated to a Primary User (PU) and uses it without harmful interference to the PU. Cognitive radio increases spectrum usage efficiency while protecting legacy-licensed systems. The purpose of this thesis is to bring together a group of CR concepts and explore how we can make the transition from conventional radio to cognitive radio. Specific goals of the thesis are firstly the measurement of the radio spectrum to understand the current spectrum usage in the Humber region, UK in the context of cognitive radio. Secondly, to characterise the performance of cyclostationary feature detectors through theoretical analysis, hardware implementation, and real-time performance measurements. Thirdly, to mitigate the effect of degradation due to multipath fading and shadowing, the use of -wideband cooperative sensing techniques using adaptive sensing technique and multi-bit soft decision is proposed, which it is believed will introduce more spectral opportunities over wider frequency ranges and achieve higher opportunistic aggregate throughput.Understanding spectrum usage is the first step toward the future deployment of cognitive radio systems. Several spectrum usage measurement campaigns have been performed, mainly in the USA and Europe. These studies show locality and time dependence. In the first part of this thesis a spectrum usage measurement campaign in the Humber region, is reported. Spectrum usage patterns are identified and noise is characterised. A significant amount of spectrum was shown to be underutilized and available for the secondary use. The second part addresses the question: how can you tell if a spectrum channel is being used? Two spectrum sensing techniques are evaluated: Energy Detection and Cyclostationary Feature Detection. The performance of these techniques is compared using the measurements performed in the second part of the thesis. Cyclostationary feature detection is shown to be more robust to noise. The final part of the thesis considers the identification of vacant channels by combining spectrum measurements from multiple locations, known as cooperative sensing. Wideband cooperative sensing is proposed using multi resolution spectrum sensing (MRSS) with a multi-bit decision technique. Next, a two-stage adaptive system with cooperative wideband sensing is proposed based on the combination of energy detection and cyclostationary feature detection. Simulations using the system above indicate that the two-stage adaptive sensing cooperative wideband outperforms single site detection in terms of detection success and mean detection time in the context of wideband cooperative sensing

CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS

Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets. Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology. Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air. This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation. 

CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS

Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets. Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology. Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air. This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation. 

CYCLOSTATIONARY DETECTION FOR OFDM IN COGNITIVE RADIO SYSTEMS

Research on cognitive radio systems has attracted much interest in the last 10 years. Cognitive radio is born as a paradigm and since then the idea has seen contribution from technical disciplines under different conceptual layers. Since then improvements on processing capabilities have supported the current achievements and even made possible to move some of them from the research arena to markets. Cognitive radio implies a revolution that is even asking for changes in current business models, changes at the infrastructure levels, changes in legislation and requiring state of the art technology. Spectrum sensing is maybe the most important part of the cognitive radio system since it is the block designed to detect signal presence on the air. This thesis investigates what cognitive radio systems require, focusing on the spectrum sensing device. Two voice applications running under different Orthogonal Frequency Division Multiplexing (OFDM) schemes are chosen. These are WiFi and Wireless Microphone. Then, a Cyclostationary Spectrum Sensing technique is studied and applied to define a device capable of detecting OFDM signals in a noisy environment. One of the most interesting methodologies, in terms of complexity and computational requirements, known as FAM is developed. Study of the performance and frequency synchronization results are shown, including the development of a blind synchronization technique for offset estimation. 

Detection of multivariate cyclostationarity

This paper derives an asymptotic generalized likelihood ratio test (GLRT) and an asymptotic locally most powerful invariant test (LMPIT) for two hypothesis testing problems: 1) Is a vector-valued random process cyclostationary (CS) or is it wide-sense stationary (WSS)? 2) Is a vector-valued random process CS or is it nonstationary? Our approach uses the relationship between a scalar-valued CS time series and a vector-valued WSS time series for which the knowledge of the cycle period is required. This relationship allows us to formulate the problem as a test for the covariance structure of the observations. The covariance matrix of the observations has a block-Toeplitz structure for CS and WSS processes. By considering the asymptotic case where the covariance matrix becomes block-circulant we are able to derive its maximum likelihood (ML) estimate and thus an asymptotic GLRT. Moreover, using Wijsman's theorem, we also obtain an asymptotic LMPIT. These detectors may be expressed in terms of the Loe`ve spectrum, the cyclic spectrum, and the power spectral density, establishing how to fuse the information in these spectra for an asymptotic GLRT and LMPIT. This goes beyond the state-of-the-art, where it is common practice to build detectors of cyclostationarity from ad-hoc functions of these spectra.The work of P. Schreier was supported by the Alfried Krupp von Bohlen und Halbach Foundation, under its program “Return of German scientists from abroad”. The work of I. Santamaría and J. Vía was supported by the Spanish Government, Ministerio de Ciencia e Innovación (MICINN), under project RACHEL (TEC2013-47141-C4-3-R). The work of L. Scharf was supported by the Airforce Office of Scientific Research under contract FA9550-10-1-0241

LMPIT-inspired Tests for Detecting a Cyclostationary Signal in Noise with Spatio-Temporal Structure

In spectrum sensing for cognitive radio, the presence of a primary user can be detected by making use of the cyclostationarity property of digital communication signals. For the general scenario of a cyclostationary signal in temporally colored and spatially correlated noise, it has previously been shown that an asymptotic generalized likelihood ratio test (GLRT) and locally most powerful invariant test (LMPIT) exist. In this paper, we derive detectors for the presence of a cyclostationary signal in various scenarios with structured noise. In particular, we consider noise that is temporally white and/or spatially uncorrelated. Detectors that make use of this additional information about the noise process have enhanced performance. We have previously derived GLRTs for these specific scenarios; here, we examine the existence of LMPITs. We show that these exist only for detecting the presence of a cyclostationary signal in spatially uncorrelated noise. For white noise, an LMPIT does not exist. Instead, we propose tests that approximate the LMPIT, and they are shown to perform well in simulations. Finally, if the noise structure is not known in advance, we also present hypothesis tests using our framework

An asymptotic LMPI test for cyclostationarity detection with application to cognitive radio

We propose a new detector of primary users in cognitive radio networks. The main novelty of the proposed detector in comparison to most known detectors is that it is based on sound statistical principles for detecting cyclostationary signals. In particular, the proposed detector is (asymptotically) the locally most powerful invariant test, i.e. the best invariant detector for low signal-to-noise ratios. The derivation is based on two main ideas: the relationship between a scalar-valued cyclostationary signal and a vector-valued wide-sense stationary signal, and Wijsman's theorem. Moreover, using the spectral representation for the cyclostationary time series, the detector has an insightful interpretation, and implementation, as the broadband coherence between frequencies that are separated by multiples of the cycle frequency. Finally, simulations confirm that the proposed detector performs better than previous approaches.The work of P. Schreier was supported by the Alfried Krupp von Bohlen und Halbach Foundation, under its program “Return of German scientists from abroad”. The work of I. Santamaría and J. Vía was supported by the Spanish Government, Ministerio de Ciencia e Innovación (MICINN), under project RACHEL (TEC2013-47141-C4-3-R). The work of L. Scharf was supported by the Airforce Office of Scientific Research under contract FA9550-10-1-0241

Doppler Radar Vital Signs Detection Method Based on Higher Order Cyclostationary

Due to the non-contact nature, using Doppler radar sensors to detect vital signs such as heart and respiration rates of a human subject is getting more and more attention. However, the related detection-method research meets lots of challenges due to electromagnetic interferences, clutter and random motion interferences. In this paper, a novel third-order cyclic cummulant (TOCC) detection method, which is insensitive to Gaussian interference and non-cyclic signals, is proposed to investigate the heart and respiration rate based on continuous wave Doppler radars. The k-th order cyclostationary properties of the radar signal with hidden periodicities and random motions are analyzed. The third-order cyclostationary detection theory of the heart and respiration rate is studied. Experimental results show that the third-order cyclostationary approach has better estimation accuracy for detecting the vital signs from the received radar signal under low SNR, strong clutter noise and random motion interferences